Transformation of network data into complex networks: problems and prospects

The article presents several ways to transform various practices into complex networks, affecting graphtheory. The purpose of the study is to provide the benefit of using a network approach to study the system. Network science attracts scientists with its wide application in various types of activities. The article shows the interpretation of some objects, processes, data with their internal nature in the network. On the contrary, for data that is very difficult to interpret in complex networks (NUD), i.e. spatial and temporal, due to their diversity, and many scientists are faced with the problem of selecting a conversion algorithm. In the course of the study, a three-stage algorithm of network optimization was used. We have identified the main properties of the data in accordance with their scale differences - in distance, time and nature, and proposed a threestage algorithm (scale-based method) to preserve the real characteristics of the practice in the interpretation of a complex network. We tested this technique on the landscape and land use maps representing the Russian Federation near Lake Baikal, Olkhonsky district, Irkutsk region. This revealed that the generalization of maps introduces some details and modified network prints, but does not lead to a significant transformation of the network topology. It is also considered as a type of augmented indexing of big data using network indicators provides high search performance in a given domain. The main conclusion in the article is that it is important to take into account the nature and features of the network, as opposed to data, considering them as networks.